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spl-to-apl

clawdbot/clawdhub/.agents/skills/spl-to-apl/SKILL.md

Translates Splunk SPL queries to Axiom APL. Provides command mappings, function equivalents, and syntax transformations. Use when migrating from Splunk, converting SPL queries, or learning APL equivalents of SPL patterns.

Skill9.5k starsChanged 2 days ago

What's in it

  1. SPL to APL Translator
  2. Critical Differences
  3. Core Command Mappings
  4. Stats → Summarize
  5. Key function mappings
  6. Conditional count pattern
  7. Eval → Extend
  8. Key function mappings
  9. Case statement pattern
  10. Rex → Parse/Extract
  11. Simple pattern (non-regex)
  12. Time Handling
  13. Timechart translation
  14. Common Patterns
  15. Error rate calculation
  16. Subquery (subsearch)
  17. Join datasets
  18. Transaction-like grouping
  19. String Matching Performance
  20. Reference
---
name: spl-to-apl
description: Translates Splunk SPL queries to Axiom APL. Provides command mappings, function equivalents, and syntax transformations. Use when migrating from Splunk, converting SPL queries, or learning APL equivalents of SPL patterns.
---

# SPL to APL Translator

**Type safety:** Fields like status are often stored as strings. Always cast before numeric comparison: toint(status) >= 500, not status >= 500.

---

## Critical Differences

1. **Time is explicit in APL:** SPL time pickers don't translate — add `where _time between (ago(1h) .. now())`
2. **Structure:** SPL `index=... | command` → APL `['dataset'] | operator`
3. **Join is preview:** limited to 50k rows, inner/innerunique/leftouter only
4. **cidrmatch args reversed:** SPL `cidrmatch(cidr, ip)` → APL `ipv4_is_in_range(ip, cidr)`

---

## Core Command Mappings

| SPL | APL | Notes |
|-----|-----|-------|
| `search index=...` | `['dataset']` | Dataset replaces index |
| `search field=value` | `where field == "value"` | Explicit where |
| `where` | `where` | Same |
| `stats` | `summarize` | Different aggregation syntax |
| `eval` | `extend` | Create/modify fields |
| `table` / `fields` | `project` | Select columns |
| `fields -` | `project-away` | Remove columns |
| `rename x as y` | `project-rename y = x` | Rename |
| `sort` / `sort -` | `order by ... asc/desc` | Sort |
| `head N` | `take N` | Limit rows |
| `top N field` | `summarize count() by field \| top N by count_` | Two-step |
| `dedup field` | `summarize arg_max(_time, *) by field` | Keep latest |
| `rex` | `parse` or `extract()` | Regex extraction |
| `join` | `join` | **Preview feature** |
| `append` | `union` | Combine datasets |
| `mvexpand` | `mv-expand` | Expand arrays |
| `timechart span=X` | `summarize ... by bin(_time, X)` | Manual binning |
| `rare N field` | `summarize count() by field \| order by count_ asc \| take N` | Bottom N |
| `spath` | `parse_json()` or `json['path']` | JSON access |
| `transaction` | No direct equivalent | Use summarize + make_list |

Complete mappings: `reference/command-mapping.md`

---

## Stats → Summarize

```
# SPL
| stats count by status

# APL  
| summarize count() by status
```

### Key function mappings

| SPL | APL |
|-----|-----|
| `count` | `count()` |
| `count(field)` | `countif(isnotnull(field))` |
| `dc(field)` | `dcount(field)` |
| `avg/sum/min/max` | Same |
| `median(field)` | `percentile(field, 50)` |
| `perc95(field)` | `percentile(field, 95)` |
| `first/last` | `arg_min/arg_max(_time, field)` |
| `list(field)` | `make_list(field)` |
| `values(field)` | `make_set(field)` |

### Conditional count pattern

```
# SPL
| stats count(eval(status>=500)) as errors by host

# APL
| summarize errors = countif(status >= 500) by host
```

Complete function list: `reference/function-mapping.md`

---

## Eval → Extend

```
# SPL
| eval new_field = old_field * 2

# APL
| extend new_field = old_field * 2
```

### Key function mappings

| SPL | APL | Notes |
|-----|-----|-------|
| `if(c, t, f)` | `iff(c, t, f)` | Double 'f' |
| `case(c1,v1,...)` | `case(c1,v1,...,default)` | Requires default |
| `len(str)` | `strlen(str)` | |
| `lower/upper` | `tolower/toupper` | |
| `substr` | `substring` | 0-indexed in APL |
| `replace` | `replace_string` | |
| `tonumber` | `toint/tolong/toreal` | Explicit types |
| `match(s,r)` | `s matches regex "r"` | Operator |
| `split(s, d)` | `split(s, d)` | Same |
| `mvjoin(mv, d)` | `strcat_array(arr, d)` | Join array |
| `mvcount(mv)` | `array_length(arr)` | Array length |

### Case statement pattern

```
# SPL
| eval level = case(
    status >= 500, "error",
    status >= 400, "warning",
    1==1, "ok"
  )

# APL  
| extend level = case(
    status >= 500, "error",
    status >= 400, "warning",
    "ok"
  )
```

Note: SPL's `1==1` catch-all becomes implicit default in APL.

---

## Rex → Parse/Extract

```
# SPL
| rex field=message "user=(?<username>\w+)"

# APL - parse with regex
| parse kind=regex message with @"user=(?P<username>\w+)"

# APL - extract function  
| extend username = extract("user=(\\w+)", 1, message)
```

### Simple pattern (non-regex)

```
# SPL
| rex field=uri "^/api/(?<version>v\d+)/(?<endpoint>\w+)"

# APL
| parse uri with "/api/" version "/" endpoint
```

---

## Time Handling

SPL time pickers don't translate. Always add explicit time range:

```
# SPL (time picker: Last 24 hours)
index=logs

# APL
['logs'] | where _time between (ago(24h) .. now())
```

### Timechart translation

```
# SPL
| timechart span=5m count by status

# APL
| summarize count() by bin(_time, 5m), status
```

---

## Common Patterns

### Error rate calculation

```
# SPL
| stats count(eval(status>=500)) as errors, count as total by host
| eval error_rate = errors/total*100

# APL
| summarize errors = countif(status >= 500), total = count() by host
| extend error_rate = toreal(errors) / total * 100
```

### Subquery (subsearch)

```
# SPL
index=logs [search index=errors | fields user_id | format]

# APL
let error_users = ['errors'] | where _time between (ago(1h) .. now()) | distinct user_id;
['logs']
| where _time between (ago(1h) .. now())
| where user_id in (error_users)
```

### Join datasets

```
# SPL
| join user_id [search index=users | fields user_id, name]

# APL
| join kind=inner (['users'] | project user_id, name) on user_id
```

### Transaction-like grouping

```
# SPL
| transaction session_id maxspan=30m

# APL (no direct equivalent — reconstruct with summarize)
| summarize 
    start_time = min(_time),
    end_time = max(_time),
    events = make_list(pack("time", _time, "action", action)),
    duration = max(_time) - min(_time)
  by session_id
| where duration <= 30m
```

---

## String Matching Performance

| SPL | APL | Speed |
|-----|-----|-------|
| `field="value"` | `field == "value"` | **Fastest** |
| `field="*value*"` | `field contains "value"` | Moderate |
| `field="value*"` | `field startswith "value"` | Fast |
| `match(field, regex)` | `field matches regex "..."` | **Slowest** |

Prefer `has` over `contains` (word-boundary matching is faster). Use `_cs` variants for case-sensitive (faster).

---

## Reference

- `reference/command-mapping.md` — complete command list
- `reference/function-mapping.md` — complete function list  
- `reference/examples.md` — full query translation examples
- APL docs: https://axiom.co/docs/apl/introduction

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